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gmonkmangmonkman
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‎funclib/numericslib.py‎

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'''basic number related helper functions'''
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import math as _math
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def translate_scale(val_in, out_min, out_max, val_in_max):
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'''(float, float, float, float) -> float
@@ -18,8 +20,6 @@ def translate_scale(val_in, out_min, out_max, val_in_max):
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return val_in*(out_max - out_min)*(1/val_in_max) + out_min
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def is_float(test):
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'''(any) -> bool
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Return true of false if s is a float
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return True
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except ValueError:
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return False
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def roundx(v):
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'''(float)->float
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round to the more extreme value
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'''
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if v < 0:
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return _math.floor(v)
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return _math.ceil(v)

‎jupyter/Untitled.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"from sklearn.svm import OneClassSVM\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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"import matplotlib.font_manager\n",
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"\n",
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"from scipy import optimize\n",
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"import pandas as pd\n",
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"import seaborn as sns\n",
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"from funclib.iolib import folder_open\n",
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"from dblib import mssql\n",
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"\n",
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"\n",
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"from sklearn.covariance import EllipticEnvelope\n",
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"from sklearn.svm import OneClassSVM\n",
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"import matplotlib.pyplot as plt\n",
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"import matplotlib.font_manager\n",
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"from sklearn.datasets import load_boston\n",
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"\n",
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"\n",
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"INCH = 2.54\n",
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"\n",
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"sns.set()\n",
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"\n",
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"#gey = [\"#FFFFFF\", \"#999999\", \"#666666\", \"#333333\", \"#000000\"]\n",
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"#grey = [\"#FFFFFF\", \"#111111\"]\n",
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"#sns.set_palette(sns.color_palette(\"cubehelix\", 8))\n",
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"\n",
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"sns.set(font=\"Times New Roman\", font_scale=1.2, rc={\"lines.linewidth\": 1})\n",
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"sns.set_style('ticks') #rc={'axes.grid':True}\n",
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"\n",
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"def cm2inch1(v):\n",
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" '''(int|float)->int|float\n",
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" '''\n",
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" return v / INCH\n",
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"\n",
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"INCH = 2.54\n",
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"\n",
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"class FigWidths(Enum):\n",
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" minimal = 3\n",
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" single_col = 9\n",
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" one_and_a_half_col = 14\n",
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" two_col = 19\n",
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"\n",
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"def getwidth(sz, as_inch=True):\n",
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" '''(Enum:FigWidths|float, bool)->float\n",
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"\n",
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" Get publication fig widths in cm or inches\n",
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" '''\n",
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" assert isinstance(sz, FigWidths)\n",
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" return sz.value/INCH if as_inch else sz.value\n",
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"\n",
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"def getheight(width, aspect, width_is_cm=True):\n",
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" '''float|Enum:plotlib.FigWidths, float|None\n",
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" Get width in inches according to target aspect\n",
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"\n",
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" width:target width, or Enum instance plotlib.FigWidths\n",
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" aspect:ratio of width to height i.e. w/h\n",
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" '''\n",
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" assert isinstance(width, (float, int, FigWidths))\n",
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"\n",
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" if isinstance(width, FigWidths):\n",
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" w_inch = width.value / INCH\n",
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" else:\n",
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" w_inch = width / INCH if width_is_cm else width\n",
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" return w_inch / aspect\n",
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" \n",
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"def label_point(x, y, val, ax):\n",
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" a = pd.concat({'x': x, 'y': y, 'val': val}, axis=1)\n",
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" for i, point in a.iterrows():\n",
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" ax.text(point['x'], point['y'], str(point['val']))\n",
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" \n",
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" \n",
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" \n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"\n",
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"# Get data\n",
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"X2 = load_boston()['data'][:, [5, 12]] # \"banana\"-shaped\n",
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"\n",
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"# Define \"classifiers\" to be used\n",
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"classifiers = {\"OCSVM\": OneClassSVM(nu=0.261, gamma=0.05)}\n",
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"colors = ['m', 'g', 'b']\n",
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"legend1 = {}\n",
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"legend2 = {}\n",
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"\n",
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"# Learn a frontier for outlier detection with several classifiers\n",
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"xx2, yy2 = np.meshgrid(np.linspace(3, 10, 500), np.linspace(-5, 45, 500))\n",
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"for i, (clf_name, clf) in enumerate(classifiers.items()):\n",
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" plt.figure(2)\n",
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" clf.fit(X2)\n",
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" Z2 = clf.decision_function(np.c_[xx2.ravel(), yy2.ravel()])\n",
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" Z2 = Z2.reshape(xx2.shape)\n",
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" legend2[clf_name] = plt.contour(xx2, yy2, Z2, levels=[0], linewidths=2, colors=colors[i])\n",
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"\n",
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"legend1_values_list = list(legend1.values())\n",
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"legend1_keys_list = list(legend1.keys())\n",
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"\n",
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"# Plot the results (= shape of the data points cloud)\n",
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"plt.figure(1) # two clusters\n",
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"plt.title(\"Outlier detection on a real data set (boston housing)\")\n",
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"plt.scatter(X1[:, 0], X1[:, 1], color='black')\n",
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"bbox_args = dict(boxstyle=\"round\", fc=\"0.8\")\n",
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"arrow_args = dict(arrowstyle=\"->\")\n",
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"plt.annotate(\"several confounded points\", xy=(24, 19),\n",
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" xycoords=\"data\", textcoords=\"data\",\n",
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" xytext=(13, 10), bbox=bbox_args, arrowprops=arrow_args)\n",
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"plt.xlim((xx1.min(), xx1.max()))\n",
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"plt.ylim((yy1.min(), yy1.max()))\n",
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"plt.legend((legend1_values_list[0].collections[0],\n",
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" legend1_values_list[1].collections[0],\n",
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" legend1_values_list[2].collections[0]),\n",
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" (legend1_keys_list[0], legend1_keys_list[1], legend1_keys_list[2]),\n",
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" loc=\"upper center\",\n",
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" prop=matplotlib.font_manager.FontProperties(size=12))\n",
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"plt.ylabel(\"accessibility to radial highways\")\n",
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"plt.xlabel(\"pupil-teacher ratio by town\")\n",
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"\n",
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"legend2_values_list = list(legend2.values())\n",
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"legend2_keys_list = list(legend2.keys())\n",
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"\n",
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"plt.figure(2) # \"banana\" shape\n",
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"plt.title(\"Outlier detection on a real data set (boston housing)\")\n",
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"plt.scatter(X2[:, 0], X2[:, 1], color='black')\n",
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"plt.xlim((xx2.min(), xx2.max()))\n",
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"plt.ylim((yy2.min(), yy2.max()))\n",
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"plt.legend((legend2_values_list[0].collections[0],\n",
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" legend2_values_list[1].collections[0],\n",
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" legend2_values_list[2].collections[0]),\n",
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" (legend2_keys_list[0], legend2_keys_list[1], legend2_keys_list[2]),\n",
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" loc=\"upper center\",\n",
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" prop=matplotlib.font_manager.FontProperties(size=12))\n",
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"plt.ylabel(\"% lower status of the population\")\n",
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"plt.xlabel(\"average number of rooms per dwelling\")\n",
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"\n",
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"plt.show()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.5.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎jupyter/Untitled1.ipynb‎

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‎jupyter/model_rotation_error.ipynb‎

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‎jupyter/mv_facet_scatter_cnn_iou.ipynb‎

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‎jupyter/mv_graph_scale.ipynb‎

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‎jupyter/mv_length_model.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.5.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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